import torch
from torch import nn
from d2l import torch as d2l
from IPython import display

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)


class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.flatten = nn.Flatten()
        self.model = nn.Linear(784, 10, bias=True)
        self.image = nn.Linear(10, 784, bias=True)

    def forward(self, input):
        output = self.flatten(input)
        output = self.model(output)
        self.imgout = imgout = self.image(output)
        return output, imgout


def train_epoch_ch3(net, train_iter, loss, l1loss, updater):  # @save
    """训练模型一个迭代周期（定义见第3章）。"""
    # 将模型设置为训练模式
    if isinstance(net, torch.nn.Module):
        net.train()
    # 训练损失总和、训练准确度总和、样本数
    metric = Accumulator(3)
    for X, y in train_iter:
        # 计算梯度并更新参数
        y_hat, _ = net(X)
        reg_loss = 0
        for param in net.parameters():
            reg_loss += torch.sum(torch.abs(param))

        l = loss(y_hat, y) + 0.01 * reg_loss + l1loss(X.view(-1, 784), net.imgout)
        #         l = loss(y_hat, y)
        if isinstance(updater, torch.optim.Optimizer):
            # 使用PyTorch内置的优化器和损失函数
            updater.zero_grad()
            l.backward()
            updater.step()
            metric.add(float(l) * len(y), accuracy(y_hat, y),
                       y.size().numel())
        else:
            # 使用定制的优化器和损失函数
            l.sum().backward()
            updater(X.shape[0])
            metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())
    # 返回训练损失和训练准确率
    return metric[0] / metric[2], metric[1] / metric[2]


class Animator:  # @save
    """在动画中绘制数据。"""

    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
                 ylim=None, xscale='linear', yscale='linear',
                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,
                 figsize=(3.5, 2.5)):
        # 增量地绘制多条线
        if legend is None:
            legend = []
        d2l.use_svg_display()
        self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)
        if nrows * ncols == 1:
            self.axes = [self.axes, ]
        # 使用lambda函数捕获参数
        self.config_axes = lambda: d2l.set_axes(
            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
        self.X, self.Y, self.fmts = None, None, fmts

    def add(self, x, y):
        # 向图表中添加多个数据点
        if not hasattr(y, "__len__"):
            y = [y]
        n = len(y)
        if not hasattr(x, "__len__"):
            x = [x] * n
        if not self.X:
            self.X = [[] for _ in range(n)]
        if not self.Y:
            self.Y = [[] for _ in range(n)]
        for i, (a, b) in enumerate(zip(x, y)):
            if a is not None and b is not None:
                self.X[i].append(a)
                self.Y[i].append(b)
        self.axes[0].cla()
        for x, y, fmt in zip(self.X, self.Y, self.fmts):
            self.axes[0].plot(x, y, fmt)
        self.config_axes()
        display.display(self.fig)
        display.clear_output(wait=True)


class Animator:  # @save
    """在动画中绘制数据。"""

    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
                 ylim=None, xscale='linear', yscale='linear',
                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,
                 figsize=(3.5, 2.5)):
        # 增量地绘制多条线
        if legend is None:
            legend = []
        d2l.use_svg_display()
        self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)
        if nrows * ncols == 1:
            self.axes = [self.axes, ]
        # 使用lambda函数捕获参数
        self.config_axes = lambda: d2l.set_axes(
            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
        self.X, self.Y, self.fmts = None, None, fmts

    def add(self, x, y):
        # 向图表中添加多个数据点
        if not hasattr(y, "__len__"):
            y = [y]
        n = len(y)
        if not hasattr(x, "__len__"):
            x = [x] * n
        if not self.X:
            self.X = [[] for _ in range(n)]
        if not self.Y:
            self.Y = [[] for _ in range(n)]
        for i, (a, b) in enumerate(zip(x, y)):
            if a is not None and b is not None:
                self.X[i].append(a)
                self.Y[i].append(b)
        self.axes[0].cla()
        for x, y, fmt in zip(self.X, self.Y, self.fmts):
            self.axes[0].plot(x, y, fmt)
        self.config_axes()
        display.display(self.fig)
        display.clear_output(wait=True)


class Accumulator:  # @save
    """在`n`个变量上累加。"""

    def __init__(self, n):
        self.data = [0.0] * n

    def add(self, *args):
        # print(self.data, args)
        self.data = [a + float(b) for a, b in zip(self.data, args)]

    def reset(self):
        self.data = [0.0] * len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]


def accuracy(y_hat, y):  # @save
    """计算预测正确的数量。"""
    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:
        y_hat = y_hat.argmax(axis=1)
    cmp = y_hat.type(y.dtype) == y
    return float(cmp.type(y.dtype).sum())


def evaluate_accuracy(net, data_iter):  # @save
    """计算在指定数据集上模型的精度。"""
    if isinstance(net, torch.nn.Module):
        net.eval()  # 将模型设置为评估模式
    metric = Accumulator(2)  # 正确预测数、预测总数
    for X, y in data_iter:
        # print(accuracy(net(X), y), y.numel())
        metric.add(accuracy(net(X)[0], y), y.numel())
    return metric[0] / metric[1]


def train_ch3(net, train_iter, test_iter, loss, l1loss, num_epochs, updater):  # @save
    """训练模型（定义见第3章）。"""
    animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.0, 10],
                        legend=['train loss', 'train acc', 'test acc'])
    for epoch in range(num_epochs):
        train_metrics = train_epoch_ch3(net, train_iter, loss, l1loss, updater)
        test_acc = evaluate_accuracy(net, test_iter)
        animator.add(epoch + 1, train_metrics + (test_acc,))
    train_loss, train_acc = train_metrics


def predict_ch3(net, test_iter, n=10):  # @save
    """预测标签（定义见第3章）。"""
    for X, y in test_iter:
        break
    trues = d2l.get_fashion_mnist_labels(y)
    preds = d2l.get_fashion_mnist_labels(net(X)[0].argmax(axis=1))
    titles = [true + '\n' + pred for true, pred in zip(trues, preds)]
    print(X[0:n].reshape((n, 28, 28)).size())
    print(net(X[0:n])[1].reshape((n, 28, 28)).size())
    d2l.show_images(
        X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])
    d2l.show_images(
        net(X[0:n]).imgout.reshape((n, 28, 28)).detach(), 1, n, titles=titles[0:n])


if __name__ == '__main__':
    net = MyModel()
    loss = nn.CrossEntropyLoss()
    l1loss = nn.L1Loss()
    trainer = torch.optim.SGD(net.parameters(), lr=0.1)
    evaluate_accuracy(net, test_iter)
    num_epochs = 100
    train_ch3(net, train_iter, test_iter, loss, l1loss, num_epochs, trainer)
    predict_ch3(net, test_iter)
